Machine learning models for predicting metabolic dysfunction-associated steatotic liver disease prevalence using basic demographic and clinical characteristics

Machine learning models for predicting metabolic dysfunction-associated steatotic liver disease prevalence using basic demographic and clinical characteristics

Abstract Background Metabolic dysfunction-associated steatotic liver disease (MASLD) is a global health concern that necessitates early screening and timely intervention to improve prognosis. The current diagnostic protocols for MASLD involve complex procedures in specialised medical centres. This s...

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Main Authors: Gangfeng Zhu, Yipeng Song, Zenghong Lu, Qiang Yi, Rui Xu, Yi Xie, Shi Geng, Na Yang, Liangjian Zheng, Xiaofei Feng, Rui Zhu, Xiangcai Wang, Li Huang, Yi Xiang
Format: Article
Language:English
Published: BMC 2025-03-01
Series:Journal of Translational Medicine
Subjects:
Metabolic dysfunction-associated steatotic liver disease
Demographic and clinical characteristics
Machine learning
Non-invasive screening
National health and nutrition examination survey
Online Access:https://doi.org/10.1186/s12967-025-06387-5
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https://doi.org/10.1186/s12967-025-06387-5

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